SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

11 January 2020

Papers citing "SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems"

22 / 22 papers shown

Title
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics Pantelis R. Vlachas Jaideep Pathak Brian R. Hunt T. Sapsis M. Girvan Edward Ott Petros Koumoutsakos AI4TS 28 386 0 09 Oct 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 122 2,082 0 08 Oct 2019
Hamiltonian Generative Networks Peter Toth Danilo Jimenez Rezende Andrew Jaegle S. Racanière Aleksandar Botev I. Higgins BDL DRL AI4CE GAN 33 216 0 30 Sep 2019
Equivariant Hamiltonian Flows Danilo Jimenez Rezende S. Racanière I. Higgins Peter Toth 29 64 0 30 Sep 2019
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 175 223 0 29 Sep 2019
Hamiltonian Graph Networks with ODE Integrators Alvaro Sanchez-Gonzalez V. Bapst Kyle Cranmer Peter W. Battaglia AI4CE 63 177 0 27 Sep 2019
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control Yaofeng Desmond Zhong Biswadip Dey Amit Chakraborty PINN 63 270 0 26 Sep 2019
Learning Symmetries of Classical Integrable Systems Roberto Bondesan A. Lamacraft 32 39 0 11 Jun 2019
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness Pengzhan Jin Lu Lu Yifa Tang George Karniadakis 18 60 0 27 May 2019
Invertible Residual Networks Jens Behrmann Will Grathwohl Ricky T. Q. Chen David Duvenaud J. Jacobsen UQCV TPM 65 621 0 02 Nov 2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 94 404 0 21 Sep 2018
Machine Learning of Space-Fractional Differential Equations Mamikon A. Gulian M. Raissi P. Perdikaris George Karniadakis 45 47 0 02 Aug 2018
Neural-net-induced Gaussian process regression for function approximation and PDE solution G. Pang Liu Yang George Karniadakis 31 73 0 22 Jun 2018
Deep Neural Networks Motivated by Partial Differential Equations Lars Ruthotto E. Haber AI4CE 53 488 0 12 Apr 2018
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems M. Raissi P. Perdikaris George Karniadakis PINN 104 265 0 04 Jan 2018
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 83 1,373 0 30 Sep 2017
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations M. Raissi George Karniadakis AI4CE PINN 36 1,132 0 02 Aug 2017
Inferring solutions of differential equations using noisy multi-fidelity data M. Raissi P. Perdikaris George Karniadakis AI4CE 36 288 0 16 Jul 2016
Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber Laurent Dinh Chi Jin Jascha Narain Sohl-Dickstein Samy Bengio Praneeth Netrapalli Aaron Sidford 149 3,670 0 26 May 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 488 149,474 0 22 Dec 2014
NICE: Non-linear Independent Components Estimation Laurent Dinh David M. Krueger Yoshua Bengio DRL BDL 69 2,246 0 30 Oct 2014
On the difficulty of training Recurrent Neural Networks Razvan Pascanu Tomas Mikolov Yoshua Bengio ODL 95 5,318 0 21 Nov 2012